Triple
T10398456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Supreme Administrative Court of the Czech Republic |
E245080
|
entity |
| Predicate | hasSeatSinceYear |
P93706
|
FINISHED |
| Object | 2003 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 2003 | Statement: [Supreme Administrative Court of the Czech Republic, hasSeatSinceYear, 2003]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatSinceYear Context triple: [Supreme Administrative Court of the Czech Republic, hasSeatSinceYear, 2003]
-
A.
seatingAddedYear
Indicates the year in which additional seating was installed or added to a venue or location.
-
B.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
C.
hasSeatAt
Indicates that an entity occupies or holds a place, position, or membership within a specific group, body, or location.
-
D.
hasSeatStatus
Indicates the current condition or availability state of a seat in a given context.
-
E.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9d1f2408190beaa8197641c66b4 |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
| PDg | Predicate description generation | batch_69d4e944fac4819093b0312aa0efd729 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:07 p.m.